Traditionally, defects in a workpiece such as wood were found by manually inspecting the workpiece. Based on that inspection and a knowledge of the type of products desired, cuts were made to the workpiece to produce products. For example, in the case of wood products, a person would inspect a piece of wood for knots and based on that inspection and a knowledge of the type of boards desired, make certain cuts to the wood.
To improve the speed and accuracy of defect detection, various machines have been developed that use cameras and computers to automatically find defects in the workpiece. The most effective of these systems create a gray scale bit mapped image of the workpiece and then test the pixels of the image against a threshold pixel intensity. Pixels that meet the threshold intensity are considered defects. These systems, however, have proven to lack consistency and accuracy when presented with the wide variations in workpiece color. For example, these systems cannot accurately distinguish heavy grain in wood from a knot. While both are darker than clear wood, only the knot is considered a defect.
Deciding what cuts to make to a workpiece has also been automated to some degree. Machines now keep track of the products cut and the production goal. This information is provided to the cutter (human or machine) to influence the cuts made to the next workpiece. For example, the production goal might be certain numbers of wood pieces of certain sizes and grades. As wood is cut into products, the numbers of wood pieces cut of each size are updated and compared to the production goal. Cuts are then made to move toward the production goal as rapidly as possible without exceeding it.
The drawback of present approaches to optimizing workpiece cutting is the time required for determining the optimum cuts. A workpiece such as wood may be cut into any number of different-sized products of different grades. A decision still must be made as to what specific cuts to make to the workpiece to provide the desired products. If the decision is by a human operator, then production is delayed. If the decision is by a human operator or a machine based solely on the information noted above, then the decision does not take into account the value of products cut from each individual workpiece.
An objective of the invention, therefore, is to improve the detection of features such as defects in a workpiece. Another objective of the invention is to optimize the cutting of a workpiece into products that provide the greatest value at the time the workpiece is cut.